Machine learning (ML) has become a critical enabler in strengthening cybersecurity systems by enhancing the ability to detect, prevent, and respond to evolving cyber threats. Traditional rule-based security mechanisms are often insufficient against sophisticated and adaptive attacks such as zero-day exploits, phishing, ransomware, and advanced persistent threats. This study explores the application of machine learning techniques in cybersecurity, focusing on their role in anomaly detection, intrusion detection systems (IDS), malware classification, phishing detection, and user behavior analytics. ML algorithms such as supervised learning, unsupervised learning, and deep learning are evaluated for their effectiveness in identifying patterns and detecting malicious activities in large-scale network data. The paper also examines the integration of ML with modern security frameworks, including Security Information and Event Management (SIEM) systems and cloud-based security platforms. Additionally, challenges such as adversarial attacks, data imbalance, model interpretability, and privacy concerns are discussed, along with emerging solutions like federated learning and explainable AI. The findings highlight that machine learning significantly improves the accuracy, speed, and adaptability of cybersecurity systems, making it an essential component of modern digital defense strategies.